The optimization of activity-travel sequences by means of reinforcement learning

Abstract

Given a sequence of activities and transport modes, this paper evaluates the use of a Reinforcement Machine Learning technique. The technique simulates time and location allocation for a given set of sequences and enables the prediction of a more complete and consistent activity pattern. A computer code has been established to automate the process and has been validated on empirical data.